Project Summary Advanced training in environmental health and data science: molecules to populations. This application represents the consolidation of three NIEHS T32 training grants at Columbia University into a unified training program designed to address critical needs in the field of environmental health sciences. We propose a program with 18 predoctoral students and 8 postdoctoral scholars. Our mentoring team has substantial funding from NIEHS (>$13,000,000 per year) and other agencies (>$40,000,0000 per year) that provides a wealth of opportunities for original research by both pre- and postdoctoral trainees. Our predoctoral trainees will participate in: 1) a core curriculum in environmental health sciences (using a life course approach to study molecular mechanisms of disease, epidemiologic methods, health effects of climate change, and the exposome) 2) a core curriculum in data sciences, 3) specialized coursework to support dissertation research, 4) research rotations, 5) small interdisciplinary training groups, and 6) dissertation research. Although our trainees will continue to take traditional didactic coursework, the addition of small interdisciplinary training groups, workshops, and boot camps creates a facile platform that can rapidly evolve to enable student exposure to cutting-edge methods that address future needs in the field. Through a collaboration with the Columbia University Data Science Institute (DSI) we propose a highly innovative training program for our postdoctoral trainees. One of the leading data science programs in the world, the Columbia DSI will provide complementary training and support for our fellows, including participation in their existing data science postdoctoral fellows program based in computer science and engineering. Fellows will acquire advanced data science skills to complement their environmental health science research (the primary focus on their training). In their second year, these fellows will enhance their leadership skills by facilitating our workshops, bootcamps, and mini-courses (machine learning, data visualization, network science) for the predoctoral trainees. Thus, the postdoctoral trainees will acquire a skillset that prepares them to apply advanced data science approaches to environmental health in the laboratory and in the classroom. Moreover, each postdoctoral fellow will lead (with an assigned faculty mentor) a small interdisciplinary training group of predoctoral trainees, providing an ongoing forum for interaction and collaboration between the pre- and postdoctoral trainees and enhancing their skills in guiding team science.